Air freshness monitoring technology based on meteorology and remote sensing
The concentrations of negative oxygen ions and particulate matter 2.5(PM2.5)serve as important indicators in the assessment of the degrees of air freshness and cleanliness.Based on 2018-2022 data from 50 negative oxygen ion observation stations affiliated with the Fujian meteorological departments,along with the ecological parameters such as aerosol,vegetation index,and surface brightness temperature obtained by satellite-based remote sensing inversion,this study built estimation models for the concentrations of negative oxygen ions and PM2.5 using the Cubist machine learning method.Accordingly,it developed an air freshness index(AFI),and the fine-scale mesh-based monitoring of regional air freshness was achieved.The results show that the estimation model for the negative oxygen ion concentration yielded goodness of fit of 0.838 and 0.526 for the training and test sets,respectively.In comparison,the estimation model for the PM2.5 concentration exhibited goodness of fit of 0.968 and 0.867 for the training and test sets,respectively.Then,this study developed the AFI by comprehensively considering negative oxygen ions and PM2.5.Then,this study graded the AFI using the frequency quartiles of the statistical data series combined with the spatiotemporal changes in negative oxygen ions.The results indicate that the AFI monitoring results based on meteorology,remote sensing,and machine learning algorithms are consistent with the actual conditions.
国家科技期刊平台
NSTL
万方数据
